CN115308661B - High frequency spark machine calibration method, device and computer storage medium - Google Patents
High frequency spark machine calibration method, device and computer storage medium Download PDFInfo
- Publication number
- CN115308661B CN115308661B CN202210881985.8A CN202210881985A CN115308661B CN 115308661 B CN115308661 B CN 115308661B CN 202210881985 A CN202210881985 A CN 202210881985A CN 115308661 B CN115308661 B CN 115308661B
- Authority
- CN
- China
- Prior art keywords
- spark machine
- information
- frequency spark
- abnormal
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000002159 abnormal effect Effects 0.000 claims abstract description 113
- 238000012545 processing Methods 0.000 claims abstract description 65
- 230000005856 abnormality Effects 0.000 claims abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 238000003066 decision tree Methods 0.000 claims description 27
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 238000011156 evaluation Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 13
- 230000035945 sensitivity Effects 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000013138 pruning Methods 0.000 claims description 4
- 238000000611 regression analysis Methods 0.000 claims description 4
- 239000000463 material Substances 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 11
- 230000015556 catabolic process Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 239000011324 bead Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23H—WORKING OF METAL BY THE ACTION OF A HIGH CONCENTRATION OF ELECTRIC CURRENT ON A WORKPIECE USING AN ELECTRODE WHICH TAKES THE PLACE OF A TOOL; SUCH WORKING COMBINED WITH OTHER FORMS OF WORKING OF METAL
- B23H1/00—Electrical discharge machining, i.e. removing metal with a series of rapidly recurring electrical discharges between an electrode and a workpiece in the presence of a fluid dielectric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Quality & Reliability (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Mechanical Engineering (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Ignition Installations For Internal Combustion Engines (AREA)
Abstract
本发明提供了一种高频火花机校准方法、装置、设备及计算机存储介质，涉及火花机校准技术领域，包括获取第一信息和第二信息，所述第一信息为高频火花机的运行视频信息，所述第二信息为所述运行视频信息对应的运行数据信息，将所述第一信息和第二信息发送至异常检测模块进行处理，得到高频火花机的异常信息；将所述异常信息发送至调整后的校准数据预测模型进行处理，得到高频火花机的校准数据；基于所述高频火花机的校准数据对所述高频火花机进行校准，得到校准后的高频火花机。本发明可以时刻监测高频火花机的运行情况，进而对高频火花机的异常数据进行校准，减少校准需要的人力物力，并且可以保障高频火花机的运行状况。
The present invention provides a high-frequency spark machine calibration method, device, equipment and computer storage medium, which relates to the technical field of spark machine calibration, including obtaining first information and second information, wherein the first information is the operation video information of the high-frequency spark machine, and the second information is the operation data information corresponding to the operation video information, sending the first information and the second information to an abnormality detection module for processing to obtain abnormal information of the high-frequency spark machine; sending the abnormal information to an adjusted calibration data prediction model for processing to obtain calibration data of the high-frequency spark machine; calibrating the high-frequency spark machine based on the calibration data of the high-frequency spark machine to obtain a calibrated high-frequency spark machine. The present invention can monitor the operation of the high-frequency spark machine at all times, and then calibrate the abnormal data of the high-frequency spark machine, reduce the manpower and material resources required for calibration, and ensure the operation of the high-frequency spark machine.
Description
Technical Field
The invention relates to the technical field of spark machine calibration, in particular to a high-frequency spark machine calibration method, a device, equipment and a computer storage medium.
Background
The existing high-frequency spark machine often has certain basic errors in the use process and generates certain abrasion in the use process, so that the high-frequency spark machine needs to be calibrated every year, a great deal of time cost and labor cost are required for operation of special personnel due to the calibration errors, the calibration frequency cannot be calibrated at any time due to the fact that the calibration time and the calibration mode further limit the calibration frequency, and a certain influence is generated on the use effect of the high-frequency spark machine, and therefore a method and a device for quickly calibrating the high-frequency spark machine are needed.
Disclosure of Invention
The invention aims to provide a high-frequency spark machine calibration method, a device, equipment and a computer storage medium, so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for calibrating a high frequency spark machine, comprising:
Acquiring first information and second information, wherein the first information is operation video information of a high-frequency spark machine, the second information is operation data information corresponding to the operation video information, and the operation data comprises a voltage value, sensitivity and stability when the high-frequency spark machine operates;
The first information and the second information are sent to an abnormality detection module for processing, and third information is obtained, wherein the third information comprises abnormality information of the high-frequency spark machine;
transmitting the third information to an adjusted calibration data prediction model for processing to obtain the calibration data of the high-frequency spark machine;
And calibrating the high-frequency spark machine based on the calibration data of the high-frequency spark machine to obtain the calibrated high-frequency spark machine.
In a second aspect, the present application also provides a high frequency spark machine calibration device, including:
The device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring first information and second information, the first information is operation video information of the high-frequency spark machine, the second information is operation data information corresponding to the operation video information, and the operation data comprises a voltage value, sensitivity and stability when the high-frequency spark machine operates;
The first processing unit is used for sending the first information and the second information to the abnormality detection module for processing to obtain third information, wherein the third information comprises the abnormality information of the high-frequency spark machine;
the second processing unit is used for sending the third information to the adjusted calibration data prediction model for processing to obtain the calibration data of the high-frequency spark machine;
and the third processing unit is used for calibrating the high-frequency spark machine based on the calibration data of the high-frequency spark machine to obtain the calibrated high-frequency spark machine.
In a third aspect, the present application also provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the high frequency spark machine based calibration method described above.
The beneficial effects of the invention are as follows:
According to the invention, the abnormal data in the operation video information of the high-frequency spark machine and the corresponding parameter information during the operation video are determined, so that the high-frequency spark machine generating the abnormal data is calculated, and then the high-frequency spark machine generating the abnormal data is calibrated based on the calculated calibration data, so that the operation condition of the high-frequency spark machine can be monitored at any time, the abnormal data of the high-frequency spark machine is calibrated, the manpower and material resources required by the calibration are reduced, and the operation condition of the high-frequency spark machine can be ensured.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for calibrating a high frequency spark machine according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a calibration device for a high-frequency spark machine according to an embodiment of the present invention.
The marks in the figure include 701, an acquisition unit, 702, a first processing unit, 703, a second processing unit, 704, a third processing unit, 7021, a first processing subunit, 7022, a first comparison subunit, 7023, a first judgment subunit, 7024, a second processing subunit, 70211, a second comparison subunit, 70212, a third processing subunit, 70213, a first calculation subunit, 70214, a second judgment subunit, 70231, an acquisition subunit, 70232, a fourth processing subunit, 70233, a third judgment subunit, 70234, a third comparison subunit, 7031, a fifth processing subunit, 7032, a second calculation subunit, 7033, and a sixth processing subunit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
As shown in fig. 1, the present embodiment provides a high-frequency spark machine calibration method, which includes step S1, step S2, step S3, and step S4.
Step S1, acquiring first information and second information, wherein the first information is operation video information of a high-frequency spark machine, the second information is operation data information corresponding to the operation video information, and the operation data comprises a voltage value, sensitivity and stability when the high-frequency spark machine operates;
it can be understood that the operation video of the high-frequency spark machine is shot through the image pickup device, wherein the operation video comprises the discharge segment of the high-frequency spark machine, whether the high-frequency spark machine can normally operate or not is judged through judging the discharge image of the high-frequency spark machine, the operation parameters corresponding to the discharge image are obtained, and whether the abnormal data caused by the parameters are judged.
Step S2, the first information and the second information are sent to an abnormality detection module to be processed, and third information is obtained, wherein the third information comprises abnormal information of the high-frequency spark machine;
It can be understood that the above steps determine an abnormal image of the discharge image of the high-frequency spark machine through image recognition, further determine a discharge angle of the high-frequency spark machine, and determine whether the high-frequency spark machine is abnormal or not according to the discharge frequency, and further determine whether the operation parameters of the high-frequency spark machine are abnormal or not according to the voltage value, sensitivity and stability of the high-frequency spark machine during operation, so as to obtain abnormal information of the high-frequency spark machine.
It can be understood that the voltage value of the high-frequency spark machine during operation is obtained by measuring through a voltmeter and uploading and storing, the sensitivity refers to that the cable with the preset breakdown times is placed on the high-frequency spark machine for detection, then the breakdown times are judged to be consistent with the preset breakdown times, and then the sensitivity is determined, the stability refers to that whether the high-frequency spark machine can normally discharge when detecting the cable without defects, the stability is 1 if the high-frequency spark machine can normally discharge, and the stability is 0 if the high-frequency spark machine cannot normally discharge.
Step S3, the third information is sent to the adjusted calibration data prediction model to be processed, and calibration data of the high-frequency spark machine are obtained;
it can be understood that regression analysis is performed through XGBoost algorithm, so that the calibration data of the high-frequency spark machine are determined, and then the discharge angle, the discharge frequency, the voltage value, the sensitivity and the stability of the high-frequency spark machine can be calibrated rapidly, and the normal operation of the high-frequency spark machine is ensured.
And S4, calibrating the high-frequency spark machine based on the calibration data of the high-frequency spark machine to obtain the calibrated high-frequency spark machine.
It can be understood that the above steps determine abnormal data by judging the operation video information of the high-frequency spark machine and the corresponding parameter information during the operation video, so as to calculate the calibration data of the high-frequency spark machine generating the abnormal data, and then calibrate the high-frequency spark machine generating the abnormal data based on the calculated calibration data, so that the operation condition of the high-frequency spark machine can be monitored at any time, the abnormal data of the high-frequency spark machine can be calibrated, the manpower and material resources required by the calibration are reduced, and the operation condition of the high-frequency spark machine can be ensured.
In a specific embodiment of the disclosure, the step S2 includes a step S21, a step S22, a step S23, and a step S24.
S21, extracting operation video information of the high-frequency spark machine according to the operation time of the high-frequency spark machine to obtain a key image of the operation video of the high-frequency spark machine, wherein the key image is a discharge image of at least one frame of the high-frequency spark machine obtained based on image identification processing;
It can be understood that the step is to identify the discharge segment of the high-frequency spark machine through image identification, so as to obtain the discharge image of each frame of high-frequency spark machine, reduce the subsequent need of judging image information, and further reduce the calculated amount.
S22, comparing a key image of the operation video of the high-frequency spark machine with a preset discharge image of the spark machine to obtain abnormal discharge image information of the high-frequency spark machine;
It can be understood that in this step, the key image is compared with a preset discharge image to determine the discharge angle and the discharge frequency, and the information is determined by the interval time of the discharge image, so as to quickly determine whether the discharge angle of the high-frequency spark machine is correct, and whether the angle of the discharge head of the spark machine and the frequency of the discharge head need to be adjusted.
Step S23, the second information is sent to a trained abnormal data judgment model to carry out abnormal judgment, so that abnormal operation data of the high-frequency spark machine are obtained;
and step S24, obtaining the abnormal information of the high-frequency spark machine based on the abnormal discharge image information of the high-frequency spark machine and the abnormal discharge image information of the high-frequency spark machine.
It can be understood that the above steps determine the abnormal information of the high-frequency spark machine by integrating the abnormal discharge image information of the high-frequency spark machine and the abnormal discharge image information of the high-frequency spark machine, wherein the abnormal information comprises abnormal operation data and abnormal discharge images, and further calculate calibration data for all the data, so that the condition that the calibration data is inaccurate due to independent calibration is prevented.
In a specific embodiment of the disclosure, the step S21 includes a step S211, a step S212, a step S213, and a step S214.
Step S211, inputting each frame of image in the key image of the operation video of the high-frequency spark machine into the image matching degree calculation model, and comparing each frame of image with a preset discharge image of the high-frequency spark machine to obtain key images of the operation video of at least two high-frequency spark machines and a preset discharge image matching degree value of the spark machine;
It can be understood that the step is that key images of operation videos of all high-frequency spark machines are respectively compared with discharge images of preset high-frequency spark machines, wherein through comparing pixel points of a discharge head and connecting the pixel points into lines, whether angles of the lines are consistent is judged, and the invention further obtains discharge frequency through comparing discharge time corresponding to the discharge images, and carries out non-dimensionality treatment on the discharge frequency and the discharge angle, then carries out association analysis based on the discharge frequency and the discharge angle pair, determines association degrees of the key images of the operation videos of the high-frequency spark machines and the discharge images of the preset high-frequency spark machines respectively, and further takes the association degree value as a matching degree value.
Step S212, summarizing key images of the operation video of all the high-frequency spark machines and preset discharge image matching degree values of the spark machines to obtain a matching degree value set;
Step S213, performing root mean square calculation on all data in the matching degree value set, and taking the obtained root mean square value as a threshold value for judging whether a key image of an operation video of the high-frequency spark machine is an abnormal discharge image or not;
And step S214, obtaining abnormal discharge image information of the high-frequency spark machine based on the threshold value for judging whether the key image of the operation video of the high-frequency spark machine is the abnormal discharge image.
It can be understood that after the key images of the operation video of all the high-frequency spark machines and the preset discharge image matching degree values of the spark machines are determined, the root mean square calculation is carried out on all the matching degree values, and the key images corresponding to the matching degree values smaller than the root mean square value are used as abnormal discharge image information of the high-frequency spark machines, so that the accuracy of judging abnormal data in the invention can be improved, and the high-frequency spark machines needing to be calibrated can be rapidly and accurately judged.
In a specific embodiment of the disclosure, the step S23 includes a step S231, a step S232, a step S233, and a step S234.
Step S231, acquiring historical operation data information, screening abnormal data of the historical operation data information, and carrying out abnormal type calibration on the abnormal data of the historical operation data information to obtain calibrated abnormal data information;
Step S232, processing the abnormal data information of the historical operation data information based on a CART algorithm to obtain a CART decision tree, and performing random pruning processing on the CART decision tree to obtain a decision tree model for judging the abnormal data information;
step S233, the historical operation data information is sent to the decision tree model for judgment, and abnormal data information of the historical operation data information is obtained;
And step S234, comparing the abnormal data information based on the historical operation data information with the calibrated abnormal data information, and adjusting the judgment parameters in the decision tree model based on the comparison result until the comparison result is the same as the preset comparison result, thereby obtaining the trained decision tree model.
It can be understood that the method performs abnormal calibration on historical data, then performs processing through the CART algorithm to establish a decision tree model, performs parameter optimization on the decision tree model, increases the judgment accuracy of the decision tree model, reduces labor cost and improves judgment efficiency.
In a specific embodiment of the disclosure, the step S3 includes a step S31, a step S32, and a step S33.
S31, carrying out regression analysis on preset historical abnormal data of the high-frequency spark machine based on XGBoost algorithm to obtain calibration data of the historical abnormal data of the high-frequency spark machine;
It can be understood that in this step, the CART tree is selected as the regression tree of the model, and then the ensemble learning is performed based on XGBoost algorithm, where the objective function of XGBoost algorithm is shown as follows, the objective function is parameterized and the tree structure is introduced into the objective function, and then the tree is continuously added to perform optimization, and a new function is added every time a tree is added.
Wherein the formula of the objective function is as follows:
Wherein obj represents the objective function of the XGBoost algorithm, L (y i, y) represents the loss function, y i represents the predicted value of the calibration data, y represents the input historical anomaly data of the high-frequency spark machine, and the square loss function is selected, i.e., L (y i,y)＝(yi-y)2, k represents a total of k trees, f k represents the function model of the kth tree, wherein Ω (f k) is a regular direction, and
Where γ and λ are both constant coefficients, T represents the number of leaf nodes per tree, ω is the set of scores of the leaf nodes per tree.
Wherein the expression of the optimization model is as follows:
wherein f t (x) represents a functional model of the t-th tree, For the optimized function model of the t-th tree,Is an optimized function model of the t-1 tree.
Step S32, calculating the calibration data of the historical abnormal data of the high-frequency spark machine based on a preset evaluation formula to obtain an evaluation value of the calibration data of the historical abnormal data of the high-frequency spark machine;
It will be appreciated that this step evaluates the calibration data by means of a mean absolute error model, wherein the mean absolute error of the predicted value of the calibration data and the historical anomaly data is calculated by means of a calculation formula for the mean absolute error, wherein a smaller MAE value represents a more accurate calibration data.
And step S33, adjusting constant coefficients in the XGBoost algorithm based on the evaluation value until the evaluation value is smaller than a preset evaluation threshold value, and obtaining an adjusted calibration data prediction model.
It can be understood that the constant coefficient in the XGBoost algorithm is repeatedly and iteratively adjusted until the calibration data is predicted to be the calibration data meeting the requirement, that is, the average absolute error value of the predicted calibration data and the historical abnormal data is smaller than the threshold value, and then the iteration is stopped to obtain the prediction model of the calibration data with the adjusted parameters.
Example 2:
As shown in fig. 2, the present embodiment provides a device for designing positions of lamp beads, which includes an acquisition unit 701, a first processing unit 702, a second processing unit 703, and a third processing unit 704.
An obtaining unit 701, configured to obtain first information and second information, where the first information is operation video information of the high-frequency spark machine, and the second information is operation data information corresponding to the operation video information, where the operation data includes a voltage value, sensitivity, and stability when the high-frequency spark machine is operated;
A first processing unit 702, configured to send the first information and the second information to an anomaly detection module for processing, to obtain third information, where the third information includes anomaly information of the high-frequency spark machine;
a second processing unit 703, configured to send the third information to the adjusted calibration data prediction model for processing, so as to obtain calibration data of the high-frequency spark machine;
And a third processing unit 704, configured to calibrate the high-frequency spark machine based on the calibration data of the high-frequency spark machine, so as to obtain a calibrated high-frequency spark machine.
In one embodiment of the disclosure, the first processing unit 702 includes a first processing subunit 7021, a first comparison subunit 7022, a first determination subunit 7023, and a second processing subunit 7024.
A first processing subunit 7021, configured to extract, according to the operation time of the high-frequency spark machine, operation video information of the high-frequency spark machine, to obtain a key image of an operation video of the high-frequency spark machine, where the key image is a discharge image of at least one frame of the high-frequency spark machine obtained based on image recognition processing;
A first comparing subunit 7022, configured to compare a key image of the operation video of the high-frequency spark machine with a preset discharge image of the spark machine, so as to obtain abnormal discharge image information of the high-frequency spark machine;
A first judging subunit 7023, configured to send the second information to the trained abnormal data judging model to perform abnormal judgment, so as to obtain abnormal operation data of the high frequency spark machine;
the second processing subunit 7024 is configured to obtain the abnormal information of the high-frequency spark machine based on the abnormal discharge image information of the high-frequency spark machine and the abnormal discharge image information of the high-frequency spark machine.
In one embodiment of the present disclosure, the first processing subunit 7021 includes a second comparison subunit 70211, a third processing subunit 70212, a first calculation subunit 70213, and a second determination subunit 70214.
The second comparison subunit 70211 is configured to input each frame of image in the key image of the operation video of the high-frequency spark machine into the image matching degree calculation model, and perform comparison processing with the preset discharge image of the high-frequency spark machine respectively to obtain a key image of the operation video of at least two high-frequency spark machines and a preset discharge image matching degree value of the spark machine;
the third processing subunit 70212 is configured to aggregate the key images of the operation video of all the high-frequency spark machines and the preset matching degree values of the discharge images of the spark machines to obtain a matching degree value set;
The first calculating subunit 70213 is configured to perform root mean square calculation on all data in the matching degree value set, and use the obtained root mean square value as a threshold value for judging whether a key image of an operation video of the high-frequency spark machine is an abnormal discharge image;
and the second judging subunit 70214 is configured to obtain abnormal discharge image information of the high-frequency spark machine based on the threshold value for judging whether the key image of the operation video of the high-frequency spark machine is an abnormal discharge image.
In one embodiment of the present disclosure, the first determination subunit 7023 includes an acquisition subunit 70231, a fourth processing subunit 70232, a third determination subunit 70233, and a third comparison subunit 70234.
The acquisition subunit 70231 is used for acquiring historical operation data information, screening abnormal data of the historical operation data information, and carrying out abnormal type calibration on the abnormal data of the historical operation data information to obtain calibrated abnormal data information;
A fourth processing subunit 70232, configured to process the abnormal data information of the historical operating data information based on a CART algorithm, obtain a CART decision tree, and perform random pruning processing on the CART decision tree to obtain a decision tree model for judging the abnormal data information;
a third judging subunit 70233, configured to send the historical operating data information to the decision tree model for judging, so as to obtain abnormal data information of the historical operating data information;
And the third comparison subunit 70234 is configured to compare the abnormal data information based on the historical operation data information with the calibrated abnormal data information, and adjust the judgment parameters in the decision tree model based on the comparison result until the comparison result is the same as the preset comparison result, thereby obtaining a trained decision tree model.
In a specific embodiment of the disclosure, the second processing unit 703 includes a fifth processing subunit 7031, a second computing subunit 7032, and a sixth processing subunit 7033.
The fifth processing subunit 7031 is configured to perform regression analysis on the preset historical anomaly data of the high-frequency spark machine based on XGBoost algorithm to obtain calibration data of the historical anomaly data of the high-frequency spark machine;
a second calculating subunit 7032, configured to calculate, based on a preset evaluation formula, calibration data of the historical abnormal data of the high-frequency spark machine, so as to obtain an evaluation value of the calibration data of the historical abnormal data of the high-frequency spark machine;
And a sixth processing subunit 7033, configured to adjust the constant coefficient in the XGBoost algorithm based on the evaluation value until the evaluation value is smaller than a preset evaluation threshold, and obtain an adjusted calibration data prediction model.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
Example 3:
corresponding to the above method embodiment, a computer storage medium is also provided in this embodiment, and a computer storage medium described below and a high-frequency spark machine calibration method described above may be referred to correspondingly.
A computer storage medium having a computer program stored thereon, which when executed by a processor performs the steps of the high frequency spark machine calibration method of the method embodiment described above.
The computer storage medium may be a usb disk, a removable hard disk, a Read-only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, where various program codes may be stored.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210881985.8A CN115308661B (en) | 2022-07-26 | 2022-07-26 | High frequency spark machine calibration method, device and computer storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210881985.8A CN115308661B (en) | 2022-07-26 | 2022-07-26 | High frequency spark machine calibration method, device and computer storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115308661A CN115308661A (en) | 2022-11-08 |
| CN115308661B true CN115308661B (en) | 2025-02-07 |
Family
ID=83858592
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210881985.8A Active CN115308661B (en) | 2022-07-26 | 2022-07-26 | High frequency spark machine calibration method, device and computer storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115308661B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118938099B (en) * | 2024-07-29 | 2025-02-07 | 江苏斯普锐特智能装备有限公司 | High-frequency spark machine calibrating device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104625276A (en) * | 2014-12-04 | 2015-05-20 | 宁波市华正信息技术有限公司 | On-line operation parameter real-time monitoring system and method of spark machine |
| CN110687409A (en) * | 2019-10-15 | 2020-01-14 | 广东省世通仪器检测服务有限公司 | AC/DC spark machine calibration method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100615682B1 (en) * | 2005-12-01 | 2006-08-25 | 미래정보산업(주) | Spark detection device for train insulation section |
| CN113516091B (en) * | 2021-07-27 | 2024-03-29 | 福建工程学院 | A method for identifying electric spark images in substations |
| CN114550063A (en) * | 2022-02-25 | 2022-05-27 | 北京掌尚信控科技有限公司 | Method, device, storage medium, device and product for identifying faults in substations |
-
2022
- 2022-07-26 CN CN202210881985.8A patent/CN115308661B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104625276A (en) * | 2014-12-04 | 2015-05-20 | 宁波市华正信息技术有限公司 | On-line operation parameter real-time monitoring system and method of spark machine |
| CN110687409A (en) * | 2019-10-15 | 2020-01-14 | 广东省世通仪器检测服务有限公司 | AC/DC spark machine calibration method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115308661A (en) | 2022-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116150676B (en) | Equipment fault diagnosis and identification method and device based on artificial intelligence | |
| CN112036426B (en) | Method and system for unsupervised anomaly detection and accountability using majority voting in high-dimensional sensor data | |
| KR101799603B1 (en) | Automatic fault detection and classification in a plasma processing system and methods thereof | |
| CN110648305A (en) | Industrial image detection method, system and computer readable recording medium | |
| US20210374634A1 (en) | Work efficiency evaluation method, work efficiency evaluation apparatus, and program | |
| CN119559382B (en) | Detection method, system, equipment and medium for intelligent welding of pipe pile joints | |
| CN110689524B (en) | No-reference online image definition evaluation method and system | |
| CN111177655B (en) | Data processing method and device and electronic equipment | |
| US20200279148A1 (en) | Material structure analysis method and material structure analyzer | |
| CN114155914B (en) | Detection and correction system based on metagenome splicing errors | |
| JP3718765B2 (en) | Plant diagnostic equipment | |
| CN115308661B (en) | High frequency spark machine calibration method, device and computer storage medium | |
| CN118037137A (en) | Method for determining product quality accident number based on convolutional neural network | |
| CN117540153A (en) | A tunnel monitoring data prediction method and system | |
| CN113051452B (en) | Operation and maintenance data feature selection method and device | |
| CN112183555B (en) | Method and system for detecting welding quality, electronic device and storage medium | |
| CN108268899A (en) | A kind of detection method of electronic component, device and equipment | |
| CN115828089B (en) | A system and method for detecting abnormal points in industrial data based on PCA algorithm | |
| CN115106615B (en) | Welding deviation real-time detection method and system based on intelligent working condition identification | |
| CN117135098A (en) | Wireless communication module qualification detection method and system based on machine learning | |
| CN114201853B (en) | Correlation analysis method between corn starch process parameters and starch milk DE value | |
| CN119128609B (en) | Data quality detection method and system applying deep learning | |
| CN119534366B (en) | Method, device, equipment and medium for detecting gas component based on spectral analysis | |
| CN118053218B (en) | Method, device and system for detecting computer board card | |
| CN120063575B (en) | High-precision adaptive pressure transmitter calibration method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |